Imm Clusters
Andrew Chen
8, August, 2024
Loading Data
imm_rpca <- readRDS(file.path(PATH, "data/sc/imm_rpca_subset.rds"))boroni_sigs <- readRDS(file.path(PATH, "data/signatures/immune/boroni_2024/pan_cancer_myeloid_sigs.rds"))
boroni_sigs <- lapply(boroni_sigs, function(x) x[x %in% rownames(imm_rpca)])
boroni_sigs <- boroni_sigs[as.numeric(lapply(boroni_sigs, length)) > 0]
immune_celltypes <- readRDS(file.path(PATH, "data/signatures/immune/combined_celltypes.rds"))
immune_celltypes <- lapply(immune_celltypes, function(x) x[x %in% rownames(imm_rpca)])
immune_pathways <- readRDS(file.path(PATH, "data/signatures/immune/immune_pathways.rds"))
immune_pathways <- lapply(immune_pathways, function(x) x[x %in% rownames(imm_rpca)])Annotations
All
imm_rpca$celltypist_imm_only <- with(imm_rpca@meta.data,
case_when(celltypist_broad == "Immune" ~ celltypist_pred,
.default = celltypist_broad))
imm_rpca$singler_pred <- str_replace(imm_rpca$singler_pred, ", alpha-beta T cell", "")
imm_rpca$singler_pred <- str_replace(imm_rpca$singler_pred, ", alpha-beta memory T cell", " memory")
imm_rpca$singler_pred <- str_replace(imm_rpca$singler_pred, " positive memory", "positive memory")
imm_rpca$singler_pred <- str_replace(imm_rpca$singler_pred, "conventional dendritic cell", "cDC")
imm_rpca$singler_pred <- str_replace(imm_rpca$singler_pred, "plasmacytoid dendritic cell", "cDC")
imm_rpca$singler_pred <- str_replace(imm_rpca$singler_pred, "cell", "")
imm_rpca$singler_imm_only <- with(imm_rpca@meta.data,
case_when(singleR_broad == "Immune" ~ singler_pred,
.default = singleR_broad))
imm_rpca$author_imm_only <- with(imm_rpca@meta.data,
case_when(author_broad == "Immune" ~ celltype_new,
.default = author_broad))
Idents(imm_rpca) <- imm_rpca$RNA_snn_res.0.4
p1 <- DimPlot_scCustom(imm_rpca,
reduction = "umap.rpca",
group.by = "RNA_snn_res.0.4",
label = TRUE,
label.size = 3,
raster = FALSE,
repel = TRUE) + labs(title = "Clustering (0.4)", x = "UMAP 1", y = "UMAP 2")
p2 <- DimPlot_scCustom(seurat_object = imm_rpca,
group.by = "celltypist_imm_only",
reduction = "umap.rpca",
label = TRUE,
label.size = 1.5,
label.box = TRUE,
repel = TRUE,
raster = FALSE) +
labs(title = "Celltypist", x = "UMAP 1", y = "UMAP 2") +
theme(legend.position = "none")
p3 <- DimPlot_scCustom(seurat_object = imm_rpca,
group.by = "singler_imm_only",
reduction = "umap.rpca",
label = TRUE,
label.size = 1.5,
label.box = TRUE,
repel = TRUE,
raster = FALSE) +
labs(title = "SingleR", x = "UMAP 1", y = "UMAP 2") +
theme(legend.position = "none")
p4 <- DimPlot_scCustom(imm_rpca,
reduction = "umap.rpca",
group.by = "author_imm_only",
label = TRUE,
label.size = 1.5,
label.box = TRUE,
repel = TRUE,
raster = FALSE) +
labs(title = "Author", x = "UMAP 1", y = "UMAP 2") +
theme(legend.position = "none")
combined <- cowplot::plot_grid(p1, p2, p3, p4, ncol = 2, nrow = 2)
ggsave(plot = combined, filename = file.path(PATH, "results/umaps/imm_rpca_annot_04.png"), height = 8, width = 9)
combined
NK
Cluster_Highlight_Plot(imm_rpca, reduction = "umap.rpca", cluster_name = c("23"))
FeaturePlot_scCustom(imm_rpca,
reduction = "umap.rpca",
features = "IFNG")##
## NOTE: FeaturePlot_scCustom uses a specified `na_cutoff` when plotting to
## color cells with no expression as background color separate from color scale.
## Please ensure `na_cutoff` value is appropriate for feature being plotted.
## Default setting is appropriate for use when plotting from 'RNA' assay.
## When `na_cutoff` not appropriate (e.g., module scores) set to NULL to
## plot all cells in gradient color palette.
##
## -----This message will be shown once per session.-----
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = "CD14")
T + NK
t_nk <- c("CD3E", "CD4", "CD8A", "NKG7")
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = t_nk)
names(boroni_sigs[27:37])## [1] "NK_cyto" "NKT" "TCD4_naive" "TCD8_naive" "TCD4_em"
## [6] "TCD8_em" "TCD4_ex" "TCD8_ex" "TCD4_reg" NA
## [11] NANK_rest
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`NK_rest`)
NK_cyto
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`NK_cyto`)
NKT
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`NKT`)
CD4_naive
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`TCD4_naive`)
CD8_naive
cd8_naive <- unique(c(boroni_sigs$`TCD8_naive`, immune_pathways$CD8_naive))Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = cd8_naive[1:4])
CD4_em
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`TCD4_em`)
CD8_em
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`TCD8_em`)
CD4_ex
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`TCD4_ex`)
CD8_ex
cd8_ex <- unique(c(boroni_sigs$TCD8_ex, immune_pathways$CD8_Tex))Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = cd8_ex[1:10])
CD4_reg
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`TCD4_reg`)
CD4 Tfh
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = immune_pathways$CD4_Tfh[1:4])
CD8 Predysfunctional
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = immune_pathways$CD8_predysfunc[1:4])
CD8 Dysfunctional
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = immune_pathways$CD8_dysfunctional[1:4])
CD8 Cytotoxic
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = immune_pathways$CD8_cytotoxic[1:4])
CD8 TRM
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = immune_pathways$CD8_TRM[1:4])
CD8/CD4 T sr
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = immune_pathways$CD4_CD8_Tstr[1:4])
Myeloid
phagocytes <- c("LYZ", "AIF1", "CD68")
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = phagocytes)
names(boroni_sigs)[str_detect(names(boroni_sigs), pattern = "Mono|Mac|RTM|cDC|Neutrophil")]## [1] "Mac_Alv_like" "Mac_Angio" "Mac_Hypo"
## [4] "Mac_ES" "Mac_IFN" "Mac_LA"
## [7] "Mac_AgPres" "RTM_IFN" "RTM_like_MT"
## [10] "Neutrophil_TAGLN2" "Neutrophil_MMP9" "cDC1_CLEC9A"
## [13] "cDC2_AREG" "cDC2_FCER1A" "cDC3_CD14"
## [16] "cDC4_FCGR3A" "cDC_CXCL8" "cDC2_CD207"
## [19] "cDC_LAMP3" "Mono_FCGR3A" "Mono_CD14_FOS-"
## [22] "Mono_CD14_FOS+" "Mono_IL1B" "MonoInter_CXCL10"
## [25] "MonoInter_CLEC10A"Monocytes
Mono_FCGR3A
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mono_FCGR3A`)
Mono_CD14_FOS-
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mono_CD14_FOS-`)
Mono_CD14_FOS+
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mono_CD14_FOS+`)
Mono_IL1B
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mono_IL1B`)
MonoInter_CXCL10
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`MonoInter_CXCL10`)
MonoInter_CLEC10A
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`MonoInter_CLEC10A`)
Macrophages
Mac_Alv_like
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mac_Alv_like`)
Mac_Angio
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mac_Angio`)
Mac_Hypo
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mac_Hypo`)
Mac_ES
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mac_ES`)
Mac_IFN
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mac_IFN`)
Mac_LA
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mac_LA`)
Mac_AgPres
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`Mac_AgPres`)
RTM_IFN
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`RTM_IFN`)
RTM_like_MT
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`RTM_like_MT`)
Mast Cells
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = c("TPSAB1", "CMA1"))
Neutrophils
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = c(boroni_sigs$`Neutrophil_TAGLN2`, boroni_sigs$`Neutrophil_MMP9`))
B cells
b_cells <- c("MS4A1", "MZB1")
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = b_cells)
Dendritic
dendritic <- c("LILRA4", "IRF7")
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = dendritic)
cDC1_CLEC9A
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC1_CLEC9A`)
cDC2_AREG
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC2_AREG`)
cDC2_FCER1A
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC2_FCER1A`)
cDC3_CD14
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC3_CD14`)
cDC4_FCGR3A
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC4_FCGR3A`)
cDC_CXCL8
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC_CXCL8`)
cDC2_CD207
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC2_CD207`)
cDC_LAMP3
Plot_Density_Custom(imm_rpca,
reduction = "umap.rpca",
features = boroni_sigs$`cDC_LAMP3`)